12 citations · 34 across the 7 of their papers we have counts for
7 papers
The power of dynamic social networks to predict individuals' mental health
Shikang Liu, David Hachen, Omar Lizardo +3
Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their…
Heterogeneous network approach to predict individuals' mental health
Shikang Liu, Fatemeh Vahedian, David Hachen +4
Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predic…
Long-term Compliance Habits: What Early Data Tells Us
Louis Faust, Priscilla Jiménez, David Hachen +3
The rise in popularity of physical activity trackers provides extensive opportunities for research on personal health, however, barriers such as compliance attrition can lead to su…
Gender Differences in Communication Behaviors, Spatial Proximity Patterns, and Mobility Habits
Yang Yang, Omar Lizardo, Dong Wang +4
The existence of gender differences in the structure and composition of social networks is a well established finding in the social and behavioral sciences, but researchers continu…
Lessons Learned from an Experiment in Crowdsourcing Complex Citizen Engineering Tasks with Amazon Mechanical Turk
Matthew Staffelbach, Peter Sempolinski, David Hachen +5
We investigate the feasibility of obtaining highly trustworthy results using crowdsourcing on complex engineering tasks. Crowdsourcing is increasingly seen as a potentially powerfu…
Weighted reciprocity in human communication networks
Cheng Wang, Anthony Strathman, Omar Lizardo +3
In this paper we define a metric for reciprocity---the degree of balance in a social relationship---appropriate for weighted social networks in order to investigate the distributio…
Predictors of short-term decay of cell phone contacts in a large scale communication network
Troy Raeder, Omar Lizardo, David Hachen +1
Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + Delta(t)? Previous research addressing this issue suggests…